Systems and methods for artificial intelligence-based personalized purchase recommendations

ABSTRACT

According to certain aspects of the disclosure, a computer-implemented method may be used for determining one or more vehicle recommendations. The method may include receiving data pertaining to a user&#39;s internet browsing activity. The received data may be indicative of the user&#39;s automotive vehicle preferences. The method may include comparing the received data to a collection of stored vehicle qualities. The method also may include identifying, based on the received data and the comparison of the received data to the collection of stored vehicle qualities, a vehicle characteristic of interest to the user. Using the vehicle characteristic of interest, one or more vehicle recommendations may be determined. One or more vehicle recommendations may be communicated to the user.

TECHNICAL FIELD

Various embodiments of the present disclosure relate generally toproviding purchase recommendations to users based on the user'spreferences and/or the preferences of a population, and morespecifically to artificial intelligence-based purchase recommendations.

BACKGROUND

Purchasers of relatively expensive items, such as cars, real estate,mattresses, boats, computers, etc. may conduct part or all of theirshopping for such items online, via the internet. In researching andcompleting such a purchase, a consumer may visit multiple websites insearch of appropriate information. For example, consumers may viewinventory information or perform other research regarding a purchase onmultiple websites. However, different websites may vary both in themanner in which they present information and in the scope of theinformation presented. Thus, a user may be unable to find certaininformation on a particular website and/or may be unsure of where suchinformation is located.

Furthermore, in areas of commerce such as those described above, theamount of information available may be so large as to be prohibitive foran individual consumer to obtain, analyze, and/or synthesize theinformation. Thus, consumers may make sub-optimal purchase decisions dueto a lack of accessible and/or digestible information.

The present disclosure is directed to addressing one or more of theseabove-referenced challenges. The background description provided hereinis for the purpose of generally presenting the context of thedisclosure. Unless otherwise indicated herein, the materials describedin this section are not prior art to the claims in this application andare not admitted to be prior art, or suggestions of the prior art, byinclusion in this section.

SUMMARY

According to certain aspects of the disclosure, non-transitory computerreadable media, systems, and methods are disclosed for determining oneor more recommendations. Each of the examples disclosed herein mayinclude one or more of the features described in connection with any ofthe other disclosed examples.

In one example, a computer-implemented method may be used fordetermining one or more vehicle recommendations. The method may includereceiving data pertaining to a user's internet browsing activity. Thereceived data may be indicative of the user's automotive vehiclepreferences. The method may include comparing the received data to acollection of stored vehicle qualities. The method also may includeidentifying, based on the received data and the comparison of thereceived data to the collection of stored vehicle qualities, a vehiclecharacteristic of interest to the user. Additionally, using the vehiclecharacteristic of interest, one or more vehicle recommendations may bedetermined, and the one or more vehicle recommendations may becommunicated to the user.

According to another aspect of the disclosure, a computer-implementedmethod may comprise analyzing literature pertaining to automotivevehicles. Using the analyzed literature, preferences of avehicle-purchasing population may be determined. The method also mayinclude analyzing internet browsing activity of an individual user.Using the internet browsing activity of the individual user and thedetermined preferences of the vehicle-purchasing population, one or morevehicle recommendations may be determined for the individual user. Theone or more vehicle recommendations may be communicated to theindividual user.

According to still another aspect of the disclosure, a computer systemfor providing vehicle purchase recommendations may include a memoryhaving processor-readable instructions stored therein and a processorconfigured to access the memory and execute the processor-readableinstructions to perform a plurality of functions. The functions mayinclude analyzing a first set of data from a first webpage, wherein thefirst set of data is indicative of a user's vehicle-purchasingpreferences. The functions also may include analyzing a second set ofdata from a second webpage, wherein the second set of data is indicativeof the user's vehicle-purchasing preferences. The functions further mayinclude using the analysis of the first set of data and the second setof data, generating a user profile pertaining to the user'svehicle-purchasing preferences. The functions also may include comparingthe user profile to a database of vehicle characteristics. The functionsadditionally may include using the comparison of the user profile to thedatabase of vehicle characteristics, generating a vehicle purchaserecommendation. The functions further may include communicating thevehicle purchase recommendation to the user.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 is a block diagram of an artificial intelligence system forproviding personalized purchase recommendations, according to one ormore embodiments; and

FIGS. 2-4 are flow charts showing exemplary methods for providingpersonalized purchase recommendations, according to one or moreembodiments.

DETAILED DESCRIPTION

The terminology used in this disclosure is to be interpreted in itsbroadest reasonable manner, even though it is being used in conjunctionwith a detailed description of certain specific examples of the presentdisclosure. Indeed, certain terms may even be emphasized below; however,any terminology intended to be interpreted in any restricted manner willbe overtly and specifically defined as such in this Detailed Descriptionsection. Both the foregoing general description and the followingdetailed description are exemplary and explanatory only and are notrestrictive of the features, as claimed.

In this disclosure, the term “based on” means “based at least in parton.” The singular forms “a,” “an,” and “the” include plural referentsunless the context dictates otherwise. The term “exemplary” is used inthe sense of “example” rather than “ideal.” The term “or” is meant to beinclusive and means either, any, several, or all of the listed items.The terms “comprises,” “comprising,” “includes,” “including,” or othervariations thereof, are intended to cover a non-exclusive inclusion suchthat a process, method, or product that comprises a list of elementsdoes not necessarily include only those elements, but may include otherelements not expressly listed or inherent to such a process, method,article, or apparatus. Relative terms, such as, “substantially” and“generally,” are used to indicate a possible variation of ±10% of astated or understood value.

In general, the present disclosure provides methods and systems forproviding purchase recommendations to consumers via, for example, aplug-in application, which may have chat capabilities. While automotivevehicles are referenced herein as an exemplary application for thesystems and methods described herein, it will be understood that thisdisclosure is not limited to automotive vehicles and may apply to othercontexts, such as real estate, technology, boats, mattresses, and/orother items. The systems and methods described herein may be used inorder to present information to consumers. For example, the disclosedsystems and methods may be used to present information regarding avehicle to a customer, even if the information is available on thewebpage a customer is visiting. The disclosed systems and methods alsomay learn a user's preferences over time and may deliver recommendationsto the user in accordance with those preferences. The systems andmethods disclosed herein may base these recommendations at least in partbased on information gathered regarding the vehicle-purchasingpreferences of a population. For example, a plug-in applicationaccording to the disclosure may act as an adviser to a user. While thedisclosure includes descriptions of exemplary methods, it will beunderstood that the steps of each method may be combined in variouscombinations or permutations and/or may be mixed and matched. Forexample, a step from one exemplary method may be used in conjunctionwith steps of another exemplary method.

FIG. 1 shows an exemplary system 10 for providing vehicle purchaserecommendations. Recommendation system 10 may include a softwareapplication 12 configured to perform a variety of operations, as will bediscussed in further detail below. Application 12 may provide, forexample, a plug-in application that works in conjunction with aninternet browser. For example, a user 14 may install application 12 towork alongside their internet browser or independently of their internetbrowser. Application 12 may then access browsing information for user 14across multiple websites. Additionally or alternatively, application 12may be utilized by one or more websites on a network 16. Application 12may be a plug-in application that is utilized by one or more websitesand that is usable by user 14 when user 14 visits a website usingapplication 12. Multiple users 14 may access application 12 via, forexample, network 16. While five users 14 are shown in FIG. 1, thatillustration is merely exemplary and any number of users 14 may accessapplication 12. Users 14 may be, for example, vehicle purchasers,vehicle researchers, and/or vehicle enthusiasts.

Application 12 may store data in and/or access data from a database 18.Database 18 may have any features known or to become known in the art.Information from database 18 may be utilized across various websites,whether application 12 is installed by user 14 (e.g., as a browserplug-in) or is installed by website owners on the sites themselves or byanother party (e.g., a party providing a browsing application).

Application 12 may have artificial intelligence capabilities. Forexample, application 12 may utilize a neural net (e.g., a machinelearning model modeled after a human/animal brain or network). Theneural net may consist of convolutional layers and/or fully connectedlayers. The neural net may be structured so as to have a sense of timeor trends, such as long short-term memory (LSTM) units or networks.Additionally or alternatively, application 12 may utilize other forms ofmachine learning, deep learning, and/or artificial intelligence.Application 12 may utilize any methodology that is known or becomesknown. Application 12 may be capable of upgrading over time, either viaintervention by a party such as user 14 or automatically. Duringoperation, the quantity and/or quality of capabilities of application 12may increase as application 12 gains access to more information.

Application 12 may analyze sources of information pertaining to, forexample, automotive vehicles and vehicle purchases. For example,application 12 may gather information via and/or analyze articleswritten about vehicles, reviews of vehicles, discussion forums, purchasewebsites, blog posts, or any other type of information pertinent tovehicle purchasing. For example, application 12 may include web crawler(e.g., spider) capabilities to systematically browse the internet.Application 12 may use a neural net or other artificial intelligencecapability in order to analyze a vast array of web content.

For example, application 12 may analyze all, nearly all, or asubstantial portion of articles written about vehicles in general orabout specific vehicles. Based on this analysis, application 12 maydetermine characteristics of interest to a particularvehicle-purchasing/researching community. For example, application 12may analyze interests of buyers/researchers of a certain age group,economic status, automotive enthusiasm level, family status, location,interests, hobbies, use level, etc. Application 12 may developcorrelations between vehicle purchaser/researcher characteristics withautomotive interests. For example, individuals with children may ingeneral have greater interest in vehicle safety profiles. People wholike to ski may have an interest in four-wheel drive vehicles or invehicles with cargo racks. Drivers in colder climates may prefer carswith heated seats or four-wheel drive. Application 12 may analyze andcombine different profile characteristics of a population. For example,application 12 may combine the different values of multiple categoriesof individuals in order to identify composite profiles. For example,application 12 may analyze data in order to determine interests ofpeople who have multiple characteristics such as, e.g., having youngchildren and an interest in watersports. For such people, application 12may determine that attributes such as safety and towing capability arehighly valued. Application 12 may build and/or maintain a library orotherwise store information relating to preferences of avehicle-purchasing/researching population. Application 12 may considermultiple populations and/or subpopulations (e.g., a US population, aworldwide population, a state population, a population of a certain age,a population of active car buyers, etc.)

Application 12 also may analyze particular preferences of a user 14across one or multiple websites. For example, application 12 may analyzeviewing habits of user 14 in order to determine user's 14 preferences.For example, application 12 may determine that user 14 has viewedmultiple vehicles having a shared characteristic. Application 12 maydetermine that user 14 is seeking a vehicle with such a characteristicor with a similar characteristic. Application 12 also may, for example,analyze articles that user 14 has read or analyze other information thatuser 14 has viewed. Such information may or may not be a car listing.For example, application 12 may consider that a user has read articlesand/or participated in message boards pertaining to high performancevehicles, such as sports cars. Application 12 may consider such viewinghabits and determine characteristics that user 14 is interested in basedon those page views.

Application 12 also may have chat or other capabilities, enablingapplication 12 to receive and respond to questions from user 14. Forexample, application 12 may analyze natural-language text input by user14, may identify characteristics relevant to the question and/or theinterests of user 14, and may respond to the question. A response to aquestion may utilize information present on a webpage user 14 isvisiting or may not rely on information from a presently-viewed webpage.For example, application 12 may present to user 14 information that isnot available on a webpage currently being viewed.

Application 12 may build a profile for user 14 based on browsingactivity of user 14 and/or information gleaned by analyzing literatureapplying to a population larger than the individual user 14. A profileof user 14 may include, for example, demographic information, browsinghistory, comparisons to other users 14, stated preferences, unstatedpreferences, priorities, interests, hobbies, etc. A profile of user 14may be used to generate recommendations for user 14.

FIG. 2. depicts an exemplary method 100 for providing a vehiclerecommendation. In step 110, a component of a system such as system 10(e.g., application 12) may receive data pertaining to internet browsingactivity of user 14. The received data may include any of theinformation described above, with respect to FIG. 1. Data received instep 110 may pertain to one or more webpages. For example, the datareceived in step 110 may include a history of pages visited by user 14,trends of information viewed by user 14, information from a particularwebpage viewed by user 14, qualities of a vehicle of interest to user14, demographic information related to user 14, financial informationand preferences of user 14, etc. Data received in step 110 may beindicative of one or more of current browsing activity and/or pastbrowsing activity of user 14.

Additionally, data received in step 110 may be indicative of one or moreproperties of a vehicle of interest to user 14. For example, in step110, data may be received from a webpage such as a car shopping webpage.Data may pertain to the qualities of a vehicle user 14 has viewed.Additionally or alternatively, data may pertain to articles or otherinformational webpages user 14 has viewed. Data also may pertain topersonal information and may be indicative of vehicle preferences ofuser 14.

In step 120, data obtained in step 110 may be compared to a collectionof stored vehicle qualities. For example, step 120 may make use of alibrary of information, such as that described above with regard toFIG. 1. A collection of stored vehicle qualities used in step 120 may beobtained via any of the methods described herein. For example, acollection of stored vehicle qualities may be obtained from and/orindicative of a vehicle purchasing/researching population (e.g., apopulation as a whole), subpopulation thereof, and/or from a specificuser 14. Vehicle characteristics or qualities utilized in step 120 maybe obtained via a neural net, as described above. Stored vehiclequalities may include any qualities such as safety information, handlinginformation, climate control, entertainment features, audio systeminformation, decorative features, storage features, etc. For example,application 12 may have access to information regarding every car modelfor sale in the United States (or another jurisdiction). Informationabout car models could be stored in, for example, database 18 of system10. Such information may be updated at regular intervals and/or on a“push” basis. Application 12 may associate characteristics of apopulation, user 14, or another individual with stored vehiclecharacteristics via a neural net or other such machine learningtechniques. For example, certain car models may be associated withsafety-minded consumers or with performance-focused consumers. As notedabove, artificial intelligence such as a neural net may be used toanalyze literature such as articles pertaining to vehicles and mayidentify characteristics of interest to an individual user 14 or to apopulation of individuals (which may include one or more users 14) basedon the analysis.

In step 130, a characteristic of interest to user 14 may be identified.For example, browsing activity of user 14, alone or in combination withstored vehicle qualities, may indicate that user 14 has an interest in,for example, vehicles equipped for cold weather use. Application 12 mayassociate that interest with, for example, all-wheel drive. Application12 also may associate that interest with additional characteristics suchas, for example, heated seats.

In step 140, application 12 may determine one or more vehiclerecommendations. For example, if application 12 has determined thatqualities such as all-wheel drive and heated seats are desirable to user14, application 12 may recommend a vehicle having those qualities touser 14. Application 12 also may consider multiple dimensions ofinterest. For example, a particular user 14 may desire a car that issuitable for winter driving and also has luxury entertainment features.Application 12 may provide a recommendation that satisfies all or asmany of these interests as possible. Application 12 may, throughartificial intelligence capabilities, rank or prioritize the qualitiesdesired by user 14. A vehicle recommendation of step 140 may bedetermined based on preferences of user 14 and/or preferences of avehicle-purchasing/researcher population or subpopulation. For example,a recommendation may compare preferences of user 14 to preferences of apopulation.

In step 150, application 12 may communicate or otherwise convey thevehicle recommendation of step 140 to user 14. For example, application12 may communicate the recommendation via text, photo, hyperlink, video,audio, or any other method. Application 12 may also display arecommendation on a search engine results page as, for example, anadvertisement. Where application 12 is a plug-in application or includesa plug-in application, such a recommendation may be made using, forexample, the plug-in application. Alternatively, a recommendation couldbe delivered via SMS or other message, by email, or by any othersuitable communication mechanism. Application 12 may communicate therecommendation, either simultaneously or sequentially, to multiplepeople, such as identified members of a family. Application 12 mayinclude functionality for user 14 to provide feedback to application 12regarding the recommendation. For example, if the deliveredrecommendation was not satisfactory to user 14, application 12 mayincorporate and/or take into account the feedback of user 14 in makingfuture recommendations. It is understood that step 110 may occur whileuser 14 is visiting a first webpage, and step 150 may occur while user14 is visiting a second webpage, different from the first webpage. Step110 may involve obtaining data from a plurality of webpages, and step150 may occur while user 14 is visiting a webpage that is different fromat least one of the plurality of webpages utilized in step 110.

FIG. 3 depicts a further exemplary method 200 for providing vehiclerecommendations. In step 210, a component of system 10, such asapplication 12, may analyze literature pertaining to automotivevehicles. Such analysis may include any of the steps or aspectsdiscussed above, including analysis using artificial intelligencesystems, such as neural nets. As discussed in further detail above,application 12 may “crawl” the internet or may otherwise accessliterature and other documents (e.g., blog posts, vehicle listings,social media data, etc.) regarding vehicles or other products.Application 12 may apply any suitable methods of analysis to theanalyzed data.

In step 220, a component such as application 12 may determinepreferences of a vehicle-purchasing/researching population. Step 220 mayutilize any of the processes discussed above with regard to FIGS. 1 and2 (e.g., for steps 120 and/or 130). For example, application 12 maydetermine, using a neural net or other framework, characteristics of avehicle-purchasing/researching population and associated preferences. Asdiscussed above, application 12 may consider numerous populationcharacteristics and consider overlapping or disparate interests ofpopulations with different characteristics.

In step 230, application 12 may analyze internet browsing activity of anindividual, such as user 14. Step 230 may use any of the approachesdiscussed above, e.g., with respect to step 110. For example,application 12 may analyze browsing activity of user 14 on one or morewebsites, including vehicle shopping-websites, websites containinginformation or literature pertaining to vehicles (e.g., review websites,enthusiast websites, or research websites), and/or websites not directlyrelated to vehicle purchasing. Application 12 may analyze browsingactivity of user 14 to determine demographic data, interests,priorities, budget, and/or other information pertinent to user 14.

In step 240, application 12 may determine one or more vehiclerecommendations for user 14. Step 240 may apply any of the techniquesdescribed above, such as those discussed with respect to step 140. Instep 250, a vehicle recommendation may be communicated by application 12to user 14. Step 250 may use any of the techniques described above, suchas those described with regard to step 150. As also described in furtherdetail above, application 12 may be configured to receive and answerquestions from user 14 at any point during the above process. Theinformation communicated in step 250 may be unavailable on a webpageuser 14 is currently viewing at the time the one or more vehiclerecommendations is communicated. Additionally, at any other point inmethod 200, application 12 may consider and/or communicate informationunavailable on a webpage user 14 is currently viewing.

FIG. 4 shows a further exemplary method 300 for making vehiclerecommendations. In step 310, a component of system 10, such asapplication 12, may analyze a first set of data from a first webpagethat is indicative of vehicle-purchasing preferences of user 14. Step310 may utilize any of the techniques described above, such as thosedescribed with regard to steps 110, 130, and/or 230. For example,application 12 may analyze data from a car-purchasing webpage, a webpagehaving vehicle information, or a page not relating directly to vehiclesbut that is indicative of the values, interests, demographics, orpreferences of user 14. For example, application 12 may analyze datafrom a particular vehicle that user 14 views. Alternatively oradditionally, application 12 may analyze articles read by user 14 inorder to determine the qualities of interest to user 14. Alternativelyor additionally, application 12 may analyze a webpage not directlyrelated to car-purchasing (e.g., a social media page) in order todetermine the characteristics of user 14 that may be relevant tocar-purchasing preferences.

In step 320, application 12 may analyze a second set of data from asecond webpage that is indicative of the vehicle-purchasing preferencesof user 14. Step 320 may utilize any of the techniques of step 310,described above. The first and second webpages of steps 310 and 320 maybe webpages in the same category or may be different types of webpages.For example, the first and second webpage may both be listings forspecific vehicles. Alternatively, the first webpage may be a vehiclelisting (or any other type of webpage), and the second webpage may be anarticle or a social media page (or any other type of webpage).

In step 330, application 12 may generate a profile pertaining to thevehicle-purchasing preferences of user 14. Aspects of the profile mayinclude any information discussed above with respect to FIGS. 1-3. Forexample, a profile may include, among other things, demographicinformation, budget information, hobbies, interests, family information,environmental information, entertainment preferences, featurepreferences, safety preferences, performance preferences, etc. A profilegenerated in step 330 may be further enriched over time. For example, asuser 14 visits further webpages, a profile generated for user 14 may beiteratively updated. Certain characteristics of the profile may changeover time, or additional characteristics may be added. Application 12also may provide an option for user 14 to manually input, change, orupdate certain profile information.

In step 340, a profile of user 14 may be compared to a database ofvehicle characteristics. Such a database may be developed using, forexample, the techniques of steps 210 and/or 220. Step 340 mayincorporate any of the aspects described above, such as those describedwith regard to steps 120, 130, or 230. For example, safety concerns ofuser 14 may be cross-referenced to safety concerns of a population as awhole. Application 12 may combine different characteristics and weighdifferent characteristics in different manners, depending on the userprofile, the preferences of a larger population, or other factors.

In step 350, application 12 may generate a vehicle purchaserecommendation. Step 350 may utilize any of the techniques describedabove, including those described with respect to steps 140 or 240. Instep 360, application 12 may communicate a vehicle purchaserecommendation, using any of the techniques described above, such asthose pertaining to steps 150 or 250.

It should be appreciated that in the above description of exemplaryembodiments of the invention, various features of the invention aresometimes grouped together in a single embodiment, figure, ordescription thereof for the purpose of streamlining the disclosure andaiding in the understanding of one or more of the various inventiveaspects. This method of disclosure, however, is not to be interpreted asreflecting an intention that the claimed invention requires morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive aspects lie in less than allfeatures of a single foregoing disclosed embodiment. Thus, the claimsfollowing the Detailed Description are hereby expressly incorporatedinto this Detailed Description, with each claim standing on its own as aseparate embodiment of this invention.

Furthermore, while some embodiments described herein include some butnot other features included in other embodiments, combinations offeatures of different embodiments are meant to be within the scope ofthe invention, and form different embodiments, as would be understood bythose skilled in the art. For example, in the following claims, any ofthe claimed embodiments can be used in any combination.

Thus, while certain embodiments have been described, those skilled inthe art will recognize that other and further modifications may be madethereto without departing from the spirit of the invention, and it isintended to claim all such changes and modifications as falling withinthe scope of the invention. For example, functionality may be added ordeleted from the block diagrams and operations may be interchanged amongfunctional blocks. Steps may be added or deleted to methods describedwithin the scope of the present invention.

The above disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover all suchmodifications, enhancements, and other implementations, which fallwithin the true spirit and scope of the present disclosure. Thus, to themaximum extent allowed by law, the scope of the present disclosure is tobe determined by the broadest permissible interpretation of thefollowing claims and their equivalents, and shall not be restricted orlimited by the foregoing detailed description. While variousimplementations of the disclosure have been described, it will beapparent to those of ordinary skill in the art that many moreimplementations and implementations are possible within the scope of thedisclosure. Accordingly, the disclosure is not to be restricted exceptin light of the attached claims and their equivalents.

What is claimed is:
 1. A computer-implemented method for determining oneor more vehicle recommendations, comprising: accessing a plurality ofliterature sources pertaining to vehicles, the plurality of literaturesources indicative of a plurality of population characteristics and aplurality of vehicle characteristics; inputting the plurality ofliterature sources into a neural net configured to determine groupingsof one or more of the population characteristics and one or more of thevehicle characteristics that, based on the plurality of literaturesources, are correlated with each other; using the determined groupingsto generate a library that, for each of the groupings: defines apurchaser population based on the one or more population characteristicsof the grouping; and associates the defined purchaser population withthe one or more vehicle characteristics of the grouping; receiving auser's internet browsing history; using the received internet browsinghistory, developing a profile of the user indicative of one or morepopulation characteristics of the user; comparing the one or morepopulation characteristics of the user to the population characteristicsof the plurality of purchaser populations stored in the library toidentify one or more purchaser populations sharing at least one of theone or more population characteristics of the user; identifying one ormore of the vehicle characteristics associated with the one or moreidentified purchaser populations as one or more vehicle characteristicsof interest to the user; using the one or more vehicle characteristicsof interest to the user, determining one or more vehiclerecommendations; and communicating, to a user device associated with theuser, the one or more vehicle recommendations.
 2. Thecomputer-implemented method of claim 1, wherein the received internetbrowsing history is obtained while the user is visiting a first webpage,and wherein the one or more vehicle recommendations is communicated tothe user while the user is visiting a second webpage, wherein the secondwebpage is different than the first webpage.
 3. The computer-implementedmethod of claim 1, wherein the literature includes at least one of anarticle, a blog post, a review, or a discussion forum.
 4. Thecomputer-implemented method of claim 1, further including: responding toa question from the user regarding a specific vehicle.
 5. Thecomputer-implemented method of claim 1, wherein the communicated one ormore vehicle recommendations includes information unavailable on awebpage the user is currently viewing at the time the one or morevehicle recommendations is communicated.
 6. The computer-implementedmethod of claim 1, wherein the one or more vehicle recommendations iscommunicated via a plug-in application operating on the user device. 7.The computer-implemented method of claim 1, wherein the plurality ofpopulation characteristics includes one or more of economic status,automotive enthusiasm level, family status, location, interests, orhobbies.
 8. The computer-implemented method of claim 1, wherein thevehicle characteristic includes at least one of safety information,handling information, climate control, entertainment features, audiosystem information, decorative features, or storage features.
 9. Thecomputer-implemented method of claim 1, further comprising updating thevehicle characteristics associated with the plurality of purchaserpopulations at a regular interval.
 10. The computer-implemented methodof claim 1, wherein the communicated one or more vehicle recommendationsincludes a hyperlink.
 11. The computer-implemented method of claim 1,wherein the internet browsing history is a first internet browsinghistory, further comprising updating the profile based on a secondinternet browsing history, received after the first internet browsinghistory.
 12. The computer-implemented method of claim 11, wherein theprofile is developed using the neural net.
 13. A computer-implementedmethod for determining one or more vehicle recommendations, comprising:associating characteristics of a plurality of purchaser populations witha plurality of vehicle characteristics using a neural net configured to:determine one or more population characteristics of a purchaserpopulation; determine one or more vehicle characteristics of interest tothe purchaser population based on characteristics of vehicles purchasedby the purchaser population; and develop an association between the oneor more population characteristics of the purchaser population and theone or more vehicle characteristics of interest to the purchaserpopulation; receiving, from time to time and via a plug-in applicationoperating in conjunction with an internet browser, respective datapertaining to a user's internet browsing activity in using the internetbrowser; using the received data and the neural net, generating aprofile of the user indicative of one or more population characteristicsof the user; upon each successive time respective data pertaining to theuser's internet browsing activity is received, iteratively updating theprofile of the user, using the neural net, based on the respective datapertaining to the user's internet browsing activity; comparing the oneor more population characteristics of the user to the populationcharacteristics of the plurality of purchaser populations; identifyingat least one vehicle characteristic of the plurality of vehiclecharacteristics as a vehicle characteristic of interest to the userbased on the comparison of the one or more population characteristics ofthe user to the population characteristics of the plurality of purchaserpopulations and on the association between the plurality of vehiclecharacteristics and the population characteristics of the plurality ofpurchaser populations; using the one or more identified vehiclecharacteristic of interest to the user, determining one or more vehiclerecommendations; and communicating to a user device associated with theuser the one or more vehicle recommendations.
 14. Thecomputer-implemented method of claim 13, wherein; the neural net isconfigured to determine the one or more population characteristics of apurchaser population by analyzing literature pertaining to vehicles; andthe literature includes at least one of an article, a blog post, areview, or a discussion forum.
 15. The computer-implemented method ofclaim 13, wherein the one or more population characteristics includesone or more of economic status, automotive enthusiasm level, familystatus, location, interests, or hobbies.
 16. The computer-implementedmethod of claim 13, wherein the one or more vehicle characteristicsincludes at least one of safety information, handling information,climate control, entertainment features, audio system information,decorative features, or storage features.
 17. The computer-implementedmethod of claim 13, further comprising updating the plurality of vehiclecharacteristics of interest to the plurality of purchaser populations ata regular interval.
 18. The computer-implemented method of claim 13,wherein the communicated one or more vehicle recommendations includes ahyperlink.
 19. A computer-implemented method for determining one or morevehicle recommendations, comprising: accessing a plurality of literaturesources pertaining to vehicles, the plurality of literature sourcesindicative of a plurality of population characteristics and a pluralityof vehicle characteristics; inputting the plurality of literaturesources into a neural net configured to determine groupings of one ormore of the population characteristics and one or more of the vehiclecharacteristics that, based on the plurality of literature sources, arecorrelated with each other; using the determined groupings to generate alibrary that, for each of the groupings: defines a purchaser populationbased on the one or more population characteristics of the grouping; andassociates the defined purchaser population with the one or more vehiclecharacteristics of the grouping; receiving, from time to time and via aplug-in application operating in conjunction with an internet browser,respective data pertaining to a user's internet browsing activity inusing the internet browser; using the received data and the neural net,generating a profile of the user indicative of one or more populationcharacteristics of the user; upon each successive time respective datapertaining to the user's internet browsing activity is received,iteratively updating the profile of the user, using the neural net,based on the respective data pertaining to the user's internet browsingactivity; comparing the one or more population characteristics of theuser to the population characteristics of the plurality of purchaserpopulations stored in the library to identify one or more purchaserpopulations sharing at least one of the one or more populationcharacteristics of the user; identifying one or more of the vehiclecharacteristics associated with the one or more identified purchaserpopulations as one or more vehicle characteristics of interest to theuser; using the one or more vehicle characteristics of interest to theuser, determining a first vehicle recommendation; communicating, to auser device associated with the user, the first vehicle recommendation;receiving, via the user device, feedback from the user regarding thefirst vehicle recommendation; using the feedback, determining a secondvehicle recommendation; and communicating to the user device the secondvehicle recommendation.